3D Point Cloud Classification On Modelnet40

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Overall Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Overall Accuracy	Paper Title	Repository
PCNN	92.3	Point Convolutional Neural Networks by Extension Operators	-
Point Cloud Transformer	93.2	PCT: Point cloud transformer	-
PointNet2+PointCMT	94.4	Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis	-
Point-MAE	94.0	Masked Autoencoders for Point Cloud Self-supervised Learning	-
PointConT	93.5	Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space	-
InterpCNN	93.0	Interpolated Convolutional Networks for 3D Point Cloud Understanding	-
point2vec	94.8	Point2Vec for Self-Supervised Representation Learning on Point Clouds	-
RS-CNN	92.9	Relation-Shape Convolutional Neural Network for Point Cloud Analysis	-
PointNet++	90.7	PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space	-
DSPoint	93.5	DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion	-
GBNet	93.8	Geometric Back-projection Network for Point Cloud Classification	-
PointNet + SageMix	90.3	SageMix: Saliency-Guided Mixup for Point Clouds	-
Point-M2AE	94.0	Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training	-
Point-PN	93.8	Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis	-
APES (local-based downsample)	93.5	Attention-based Point Cloud Edge Sampling	-
PointGPT	94.9	-	-
PointNeXt	94.0	PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies	-
Perceiver	-	Perceiver: General Perception with Iterative Attention	-
GDANet	93.8	Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud	-
VRN (single view)	-	Generative and Discriminative Voxel Modeling with Convolutional Neural Networks	-

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